Optimization Operation of Multi-agent Mixed Game in Community Comprehensive Energy System Considering Source-load Uncertainty

To further investigate power interaction and benefit allocation among multiple agents in a community integrated energy system with producers and consumers under source-load uncertainty, in this paper we propose a two-layer mixed game optimization operation model for the community integrated energy system, with asource load uncertainty taken into account. The first step involves establishing a two-layer hybrid game framework, with energy service providers as upper level leaders and user alliances as lower level followers. Secondly, an upper and lower level optimization model is constructed with the goal of minimizing costs. The upper level service provider guides the lower level alliance to use energy reasonably by setting energy prices, while the lower level alliance responds to the decisions of the upper level service provider through cooperative games. To ensure the reasonable allocation of benefits among various entities in the alliance, based on Nash negotiation theory, the lower level alliance optimization model is decomposed into two sub problems: alliance cost minimization and alliance energy transaction payment. Subsequently, a three-stage robust optimization model is constructed considering the uncertainty of the upper source load, and the KKT condition is utilized to transform the two-layer game problem within the model into a single-layer linearization problem. Finally, the model is solved using the column and constraint generation algorithm combined with the alternating direction multiplier method. The calculation results demonstrate that the model constructed in this paper can effectively address the uncertainty risk of upper level source loads, while achieving a reasonable allocation of alliance benefits and reduction of total community costs, as well as coordinating and optimizing the operation of all parties involved.